Temporal overview
p_year %>%
inner_join(poems,by=c("p_id")) %>%
count(collection,year) %>%
mutate(measure="yearly count") %>%
union_all(
p_year %>% # 10 year rolling mean
distinct(year) %>%
left_join(p_year %>% distinct(year),sql_on="RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
inner_join(p_year,by=c("year.y"="year")) %>%
inner_join(poems,by=c("p_id")) %>%
group_by(collection=collection,year=year.x) %>%
summarize(n=n()/10,.groups="drop") %>%
mutate(measure="10 year rolling mean")
) %>%
filter(year>0,year<9999,collection!="literary") %>%
ggplot(aes(x=year,y=n,color=measure)) +
geom_point(data=~.x %>% filter(measure=="yearly count")) +
geom_line(data=~.x %>% filter(measure=="10 year rolling mean")) +
theme_hsci_discrete(base_family="Arial") +
theme(legend.justification=c(0,1), legend.position=c(0.02, 0.98), legend.background = element_blank(), legend.key=element_blank()) +
labs(color=NULL) +
scale_y_continuous(breaks=seq(0,20000,by=2000),labels=scales::comma_format()) +
ylab("Poems") +
scale_x_continuous(breaks=seq(1000,2000,by=50)) +
xlab("Year") +
facet_wrap(~collection, ncol=1) +
ggtitle("Number of poems by year and collection")

p_year %>%
filter(year %in% c(0,9999)) %>%
left_join(poems) %>%
count(collection,year) %>%
ungroup() %>%
gt() %>%
tab_header(title=\Abnormal years\) %>%
fmt_integer(n)
Overview of collectors
poems %>%
distinct(collection) %>%
pull() %>%
map(~p_col %>%
inner_join(poems %>% filter(collection==.x),by=c("p_id")) %>%
count(col_id) %>%
left_join(collectors,by=c("col_id")) %>%
select(col_id,name,n) %>%
collect() %>%
mutate(col_id=fct_reorder(str_c(col_id,"|",name),n)) %>%
mutate(col_id=fct_lump_n(col_id,n=100,w=n)) %>%
mutate(col_id=fct_relevel(col_id,"Other")) %>%
group_by(col_id) %>%
tally(wt=n) %>% {
ggplot(.,aes(x=col_id,y=n)) +
geom_col() +
geom_text(aes(label=p(n)),hjust='left',nudge_y = 100) +
theme_hsci_discrete(base_family="Arial") +
coord_flip() +
labs(title=str_c("Collectors in ",.x))
}
)
Warning: 1 unknown level in `f`: Other
[[1]]
[[2]]
[[3]]
[[4]]




p_col %>%
anti_join(collectors) %>%
count(col_id) %>%
gt() %>%
tab_header(title=\Collectors without a name\) %>%
fmt_integer(n)
Geographical overview
d <- p_loc %>%
count(loc_id) %>%
inner_join(locations) %>%
select(name,n) %>%
collect()
poems_without_location <- poems %>%
anti_join(p_loc) %>%
count() %>%
pull()
unprojected_locations <- d %>%
anti_join(polygons) %>%
add_row(name=NA,n=poems_without_location)
polygons %>%
left_join(d) %>%
tm_shape() +
tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
tm_layout(title=str_c(\Geographical overview. Missing \,unprojected_locations %>% tally(wt=n) %>% pull() %>% p,\ poems.\))

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1 to 312
312 to 847
847 to 1,732
1,732 to 3,075
3,075 to 3,985
Missing
#0D0887
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#BFBFBF
#666666
n
n
name
to
Less
than
or
more
metric
png
Geographical overview. Missing 37,345 poems.
#FFFFFF
YlOrBr
RdYlGn
Set3
black
#000000
plain
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right
vertical
left
bottom
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to
Less
than
or
more
left
top
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left
bottom
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bottom
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bottom
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OpenStreetMap
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topright
left
top
grey85
grey40
grey60
black
black
black
grey75
grey95
Missing
dummy
.
km
none
km
km
Poem locations not mapped
unprojected_locations %>%
arrange(desc(n)) %>%
gt() %>%
tab_header(\Poem locations not mapped\) %>%
fmt_integer(n)
Geographical overview by collection
d <- p_loc %>%
left_join(poems) %>%
count(collection,loc_id) %>%
ungroup() %>%
inner_join(locations) %>%
select(collection,name,n) %>%
collect()
poems_without_location <- poems %>%
anti_join(p_loc) %>%
count(collection) %>%
collect() %>%
mutate(name=NA_character_)
unprojected_locations <- d %>%
anti_join(polygons) %>%
union_all(poems_without_location)
poems %>%
distinct(collection) %>%
pull() %>%
map(~
tm_shape(
polygons %>%
left_join(
p_loc %>%
inner_join(poems %>% filter(collection==.x),by=c(\p_id\)) %>%
count(loc_id) %>%
inner_join(locations) %>%
select(name,n) %>%
collect()
)
) +
tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
tm_layout(title=str_c(\Geography of \,.x,\. Missing \,unprojected_locations %>% filter(collection==.x) %>% tally(wt=n) %>% pull() %>% p,\ poems.\))
)
[[1]]

[[2]]

[[3]]

[[4]]

Poem locations not mapped by collection
poems %>%
distinct(collection) %>%
pull() %>%
map(~
unprojected_locations %>%
filter(collection==.x) %>%
arrange(desc(n)) %>%
select(-collection) %>%
gt() %>%
tab_header(str_c("Poem locations not mapped in ",.x)) %>%
fmt_integer(n)
)
[[1]]
| name |
n |
| Narvusi |
3,202 |
| Vuole |
1,524 |
| Uusikirkko Vpl |
1,131 |
| Pyhäjärvi Vpl |
785 |
| Etelä-Karjala |
658 |
| Pohjois-Pohjanmaa |
557 |
| Länsipohja |
396 |
| Kiimaisjärvi |
363 |
| Etelä-Savo |
350 |
| Tveri |
294 |
| Viipuri mlk |
277 |
| Salo |
198 |
| Pieksämäki |
183 |
| Pyhäjärvi Ol |
160 |
| Koski Hl |
134 |
| Pohjois-Karjala |
129 |
| Uusikirkko Tl |
116 |
| Tulomajärvi |
115 |
| Tornio |
113 |
| Porvoo mlk |
112 |
| Pyhäjärvi Ul |
102 |
| Pohjois-Savo |
98 |
| Viena |
98 |
| Satakunta |
83 |
| Säräisniemi |
74 |
| Uusikaupunki |
71 |
| Heinola mlk |
62 |
| Häme |
61 |
| Ruija |
60 |
| Jyväskylä mlk |
52 |
| Keski-Inkeri |
49 |
| Hämeenlinna |
48 |
| Kovero |
47 |
| Tuutari=Tuuteri |
45 |
| Laatokan Karjala (Raja-Karjala) |
44 |
| Itä- ja Pohjois-Inkeri |
42 |
| Kainuu |
38 |
| Kuopio mlk |
35 |
| Peräpohjola |
33 |
| Varkaus |
30 |
| Kajaani mlk |
30 |
| Etelä-Pohjanmaa |
27 |
| Keminmaa |
26 |
| Varsinais-Suomi |
24 |
| Mänttä |
22 |
| Novgorodin alue |
22 |
| Kotka |
20 |
| Mikkeli mlk |
17 |
| Karjala Tl |
16 |
| Alajärvi |
16 |
| Kimito |
15 |
| Prunkkala |
14 |
| Savonlinna |
14 |
| Sortavala mlk |
13 |
| Revonlahti-Revolax |
12 |
| Alakiiminki |
11 |
| Taipale (Enontaipale) |
9 |
| Helsingin pit |
7 |
| Sulva-Solf-Solv |
7 |
| Siuntio |
6 |
| Mustasaari-Korsholm |
6 |
| Siipyy-Sideby |
6 |
| Uzmana |
6 |
| Suma |
6 |
| Lapväärtti-Lappfjärd |
5 |
| Vaasa |
5 |
| Lappeenranta |
4 |
| Kenjärvi |
4 |
| Vammala |
3 |
| Kirkkonummi |
3 |
| Kokkola-Gamlakarleby |
3 |
| Kuolajärvi |
3 |
| Uusimaa |
2 |
| Iisalmi mlk |
2 |
| Pietarsaari-Jakobstad |
2 |
| Jepua-Jeppo |
2 |
| Kouta |
2 |
| Länsi-Inkeri |
2 |
| Uusikaupunki mlk |
1 |
| Anjalankoski |
1 |
| Keski-Suomi |
1 |
| Lieksa |
1 |
| Raippaluoto-Replot |
1 |
| Kuhmoniemi |
1 |
[[2]]
| name |
n |
| NA |
2,008 |
| välismaa |
1,822 |
| Tartu |
973 |
| Tallinn |
685 |
| Viljandi l. |
602 |
| Pärnu l. |
598 |
| Viljandimaa |
480 |
| Võrumaa |
452 |
| Narva l. |
422 |
| Läänemaa |
329 |
| Rakvere l. |
304 |
| Saaremaa |
205 |
| Valga |
150 |
| Pärnumaa |
102 |
| Paide l. |
88 |
| Haapsalu |
86 |
| Võru l. |
60 |
| Valgamaa |
58 |
| Sõrve |
40 |
| Järvamaa |
39 |
| Virumaa |
36 |
| Tartumaa |
29 |
| Hiiumaa |
23 |
| Harjumaa |
17 |
| Kuressaare l. |
3 |
[[3]]
| name |
n |
| Narvusi |
1,780 |
| NA |
1,591 |
| Peräpohjola |
809 |
| Uusikirkko Vpl |
803 |
| Viena |
773 |
| Itä- ja Pohjois-Inkeri |
757 |
| Hämeenlinna |
517 |
| Keski-Inkeri |
483 |
| Viron Inkeri |
433 |
| Länsi-Inkeri |
424 |
| Sortavala mlk |
406 |
| Uusikaupunki |
386 |
| Tulomajärvi |
353 |
| Keminmaa |
349 |
| Pohjois-Pohjanmaa |
252 |
| Savonlinna |
240 |
| Pyhäjärvi Vpl |
215 |
| Lahti |
189 |
| Pyhäjärvi Ol |
164 |
| Pieksämäki |
163 |
| Säräisniemi |
159 |
| Vuole |
155 |
| Etelä-Pohjanmaa |
154 |
| Mikkeli mlk |
144 |
| Pyhäjärvi Ul |
143 |
| Viipuri mlk |
123 |
| Kuusankoski |
115 |
| Etelä-Karjala |
112 |
| Pohjois-Karjala |
111 |
| Pohjois-Savo |
110 |
| Tveri |
110 |
| Salo |
107 |
| Riihimäki |
104 |
| Helsingin pit |
96 |
| Mänttä |
91 |
| Kiimaisjärvi |
85 |
| Lapväärtti-Lappfjärd |
82 |
| Kainuu |
81 |
| Kotka |
76 |
| Alajärvi |
75 |
| Toijala |
70 |
| Laatokan Karjala (Raja-Karjala) |
63 |
| Etelä-Savo |
62 |
| Uusikirkko Tl |
61 |
| Lappeenranta |
59 |
| Tornio |
59 |
| Heinola mlk |
55 |
| Tuutari=Tuuteri |
55 |
| Pohjois-Pirkkala |
47 |
| Uusimaa |
44 |
| Kuopio mlk |
42 |
| Karjala Tl |
35 |
| Varkaus |
32 |
| Ruija |
31 |
| Kajaani mlk |
30 |
| Porvoo mlk |
22 |
| Storfjord |
20 |
| Parainen |
18 |
| Lappi Tl |
17 |
| Jyväskylä mlk |
17 |
| Vaasa |
17 |
| Vaala |
17 |
| Karkkila |
16 |
| Kouvola |
16 |
| Rovaniemi mlk |
16 |
| Kerava |
15 |
| Valkeakoski |
15 |
| Lieksa |
15 |
| Kuusisto |
13 |
| Muoslompolo |
13 |
| Uusikaupunki mlk |
12 |
| Hongonjoki |
12 |
| Satakunta |
12 |
| Novgorodin alue |
12 |
| Kemiö |
11 |
| Salo |
11 |
| Pieksämäki mlk |
11 |
| Iisalmi mlk |
11 |
| Pielisensuu |
11 |
| Särkisalo |
10 |
| Hamina |
10 |
| Kokkola-Gamlakarleby |
10 |
| Kristiinankaupunki-Kristinestad |
10 |
| Revonlahti-Revolax |
10 |
| Nauvo |
9 |
| Junosuando |
9 |
| Karesuando |
9 |
| Jämsänkoski |
8 |
| Koski Hl |
8 |
| Äänislinna |
8 |
| Kuhmoniemi |
7 |
| Muurmanni |
7 |
| Häme |
6 |
| Suolahti |
6 |
| Taipale |
6 |
| Mustasaari-Korsholm |
6 |
| Länsipohja |
6 |
| Rauma mlk |
5 |
| Kuolajärvi |
5 |
| Kistrand |
5 |
| Moseija |
5 |
| Kenjärvi |
5 |
| Imatra |
4 |
| Uusikaarlepyy-Nykarleby |
4 |
| Siipyy-Sideby |
4 |
| Yliveteli |
4 |
| Lauritsala |
3 |
| Ruukki |
3 |
| Koutokeino |
3 |
| Riipuskala |
3 |
| Varsinais-Suomi |
2 |
| Vammala |
2 |
| Järvenpää |
2 |
| Tiudia |
2 |
| Jaama |
2 |
| Prunkkala |
1 |
| Ikaalinen mlk |
1 |
| Myllykoski |
1 |
| Kirkkonummi |
1 |
| Loviisa |
1 |
| Sipoo |
1 |
| Nurmes mlk |
1 |
| Petolahti-Petalax |
1 |
| Oulu mlk |
1 |
| Nordkapp |
1 |
| Maasöy |
1 |
| Muodoslompolo |
1 |
| Pietari=Leningrad |
1 |
| Siestarjoki |
1 |
| Ahvenanmaa |
1 |
[[4]]
NA
Spatiotemporal overview
d <- poems %>%
left_join(p_year %>% mutate(year=if_else(year %in% c(0L,9999L),NA,year))) %>%
collect() %>%
mutate(year_ntile=ntile(year,11)) %>%
group_by(year_ntile) %>%
mutate(years=str_c(min(year),\-\,max(year))) %>%
ungroup() %>%
left_join(p_loc %>% collect()) %>%
count(years,loc_id) %>%
ungroup() %>%
left_join(locations %>% select(loc_id,name) %>% collect())
polygons %>%
left_join(d %>% complete(name,years)) %>%
tm_shape() +
tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
tm_layout(main.title="Geographical overviews by time",legend.outside.size=0.1) +
tm_facets(by="years",ncol=4)
Joining, by = "name"

Poem verse statistics
Line types
d <- verses %>%
left_join(verse_poem) %>%
left_join(poems) %>%
count(collection,type) %>%
arrange(collection,desc(n)) %>%
collect()
Joining, by = "v_id"Joining, by = "p_id"
d %>%
group_by(collection) %>%
mutate(proportion=n/sum(n)) %>%
gt() %>%
fmt_integer(n) %>%
fmt_percent(proportion)
| type |
n |
proportion |
| skvr |
| V |
1,340,987 |
94.63% |
| L |
44,303 |
3.13% |
| CPT |
27,869 |
1.97% |
| K |
3,931 |
0.28% |
| erab |
| V |
1,861,583 |
93.39% |
| PAG |
53,040 |
2.66% |
| CPT |
19,844 |
1.00% |
| L |
18,465 |
0.93% |
| TYH |
18,357 |
0.92% |
| REF |
17,869 |
0.90% |
| LRY |
3,868 |
0.19% |
| RRE |
307 |
0.02% |
| MRK |
52 |
0.00% |
| U |
38 |
0.00% |
| LLI |
2 |
0.00% |
| TYP |
1 |
0.00% |
| jr |
| V |
812,343 |
90.94% |
| L |
49,411 |
5.53% |
| CPT |
28,030 |
3.14% |
| K |
3,502 |
0.39% |
| literary |
| V |
82,460 |
97.54% |
| L |
1,220 |
1.44% |
| CPT |
777 |
0.92% |
| K |
87 |
0.10% |
Verse line lengths
d_nr_characters <- verses_cl %>%
mutate(nr_characters=str_length(text)) %>%
left_join(verse_poem) %>%
left_join(poems) %>%
count(collection,nr_characters) %>%
ungroup() %>%
arrange(collection,desc(n)) %>%
collect()
Joining, by = "v_id"Joining, by = "p_id"
Verse line lengths in characters
d_nr_characters %>%
filter(nr_characters<=60) %>%
ggplot(aes(x=nr_characters,y=n)) +
geom_col(width=1) +
facet_wrap(~collection,scales="free_y") +
theme_hsci_discrete(base_family="Arial") +
scale_y_continuous(labels=scales::comma_format()) +
labs(title="Number of characters in verse lines")

d_nr_characters %>%
group_by(collection) %>%
mutate(prop=n/sum(n)) %>%
ungroup() %>%
filter(nr_characters<=60) %>%
ggplot(aes(x=nr_characters,y=collection,fill=collection,height=prop)) +
geom_density_ridges(stat='identity') +
theme_hsci_discrete(base_family="Arial") +
# scale_y_continuous(labels=scales::percent_format()) +
labs(title="Number of characters in verse lines")

Verse lines with more than 60 characters
d_nr_characters %>%
mutate(nl=if_else(nr_characters>60,n,0L)) %>%
group_by(collection) %>%
summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
arrange(desc(lines)) %>%
gt() %>%
tab_header(title="Verse lines with more than 60 characters") %>%
fmt_integer(lines) %>%
fmt_percent(proportion)
| collection |
lines |
proportion |
| jr |
1,911 |
0.24% |
| erab |
291 |
0.02% |
| skvr |
202 |
0.02% |
| literary |
1 |
0.00% |
Verse line lengths in words
d_nr_words %>%
filter(nr_words<=10) %>%
ggplot(aes(x=nr_words,y=n)) +
geom_col(width=1) +
facet_wrap(~collection,scales="free_y") +
scale_x_continuous(breaks=seq(0,10,by=2)) +
scale_y_continuous(labels=scales::comma_format()) +
theme_hsci_discrete(base_family="Arial") +
labs(title="Number of words in verse lines")

d_nr_words %>%
filter(nr_words<=10) %>%
uncount(n) %>%
ggplot(aes(x=nr_words,y=collection,fill=collection)) +
stat_binline(binwidth=1) +
theme_hsci_discrete(base_family="Arial") +
scale_x_continuous(breaks=seq(0,10,by=2)) +
# scale_y_continuous(labels=scales::percent_format()) +
labs(title="Number of words in verse lines")

Verse lines with more than 10 words
d_nr_words %>%
mutate(nl=if_else(nr_words>10,n,0L)) %>%
group_by(collection) %>%
summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
arrange(desc(lines)) %>%
gt() %>%
tab_header(title="Verse lines with more than 10 words") %>%
fmt_integer(lines) %>%
fmt_percent(proportion)
| collection |
lines |
proportion |
| jr |
839 |
0.11% |
| erab |
257 |
0.01% |
| skvr |
38 |
0.00% |
| literary |
9 |
0.01% |
d <- word_occ %>%
inner_join(words %>%
mutate(nr_characters=str_length(text))) %>%
group_by(v_id) %>%
summarise(nr_characters,nr_words=max(pos)) %>%
left_join(verse_poem) %>%
left_join(poems) %>%
count(collection,nr_words,pos,nr_characters) %>%
collect()
Joining, by = "w_id"Joining, by = "v_id"Joining, by = "p_id"Error: [0]
d %>%
group_by(collection,nr_words,pos) %>%
mutate(prop=n/sum(n)) %>%
ungroup() %>%
filter(nr_words>=2,nr_words<6) %>%
mutate(nr_words=as_factor(nr_words),pos=as_factor(pos)) %>%
uncount(n) %>%
ggplot(aes(x=nr_characters,y=nr_words,fill=nr_words)) +
stat_binline(binwidth=1) +
facet_grid(collection~pos,labeller = labeller(pos=label_both)) +
xlab("Number of characters in word") +
ylab("Number of words in verse") +
labs(
title="Number of characters in words by their position",
subtitle="According to length of verse and collection"
) +
theme_hsci_discrete(base_family="Arial")

---
title: "General statistical overviews of FILTER data"
date: "`r Sys.Date()`"
output: 
  html_notebook:
    code_folding: hide
    toc: yes
  html_document:
    code_folding: hide
    toc: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(message=FALSE,dpi=72,fig.retina=2,fig.width=8)
source(here::here("code/common_basis.R"), local = knitr::knit_global())
```

# Temporal overview

```{r temporal_overview, fig.width=8, fig.height=8}
p_year %>% 
  inner_join(poems,by=c("p_id")) %>%
  count(collection,year) %>%
  mutate(measure="yearly count") %>%
  union_all(
    p_year %>% # 10 year rolling mean
    distinct(year) %>% 
    left_join(p_year %>% distinct(year),sql_on="RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
    inner_join(p_year,by=c("year.y"="year")) %>%
    inner_join(poems,by=c("p_id")) %>%
    group_by(collection=collection,year=year.x) %>%
    summarize(n=n()/10,.groups="drop") %>%
    mutate(measure="10 year rolling mean")
  ) %>%
  filter(year>0,year<9999,collection!="literary") %>%
  ggplot(aes(x=year,y=n,color=measure)) +
  geom_point(data=~.x %>% filter(measure=="yearly count")) +
  geom_line(data=~.x %>% filter(measure=="10 year rolling mean")) +
  theme_hsci_discrete(base_family="Arial") + 
  theme(legend.justification=c(0,1), legend.position=c(0.02, 0.98), legend.background = element_blank(), legend.key=element_blank()) + 
  labs(color=NULL) +
  scale_y_continuous(breaks=seq(0,20000,by=2000),labels=scales::comma_format()) +
  ylab("Poems") +
  scale_x_continuous(breaks=seq(1000,2000,by=50)) +
  xlab("Year") +
  facet_wrap(~collection, ncol=1) +
  ggtitle("Number of poems by year and collection")
```

```{r}
p_year %>% 
  filter(year %in% c(0,9999)) %>% 
  left_join(poems) %>% 
  count(collection,year) %>%
  ungroup() %>%
  gt() %>%
  tab_header(title="Abnormal years") %>%
  fmt_integer(n)
```

# Overview of collectors

```{r collectors_overview, fig.width=8, fig.height=11}
poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~p_col %>% 
    inner_join(poems %>% filter(collection==.x),by=c("p_id")) %>%
    count(col_id) %>%
    left_join(collectors,by=c("col_id")) %>%
    select(col_id,name,n) %>%
    collect() %>%
    mutate(col_id=fct_reorder(str_c(col_id,"|",name),n)) %>%
    mutate(col_id=fct_lump_n(col_id,n=100,w=n)) %>%
    mutate(col_id=fct_relevel(col_id,"Other")) %>%
    group_by(col_id) %>%
    tally(wt=n) %>% {
      ggplot(.,aes(x=col_id,y=n)) +
      geom_col() +
      geom_text(aes(label=p(n)),hjust='left',nudge_y = 100) +
      theme_hsci_discrete(base_family="Arial") +
      coord_flip() +
      labs(title=str_c("Collectors in ",.x))
    }
  )
```

```{r}
p_col %>% 
  anti_join(collectors) %>%
  count(col_id) %>%
  gt() %>%
  tab_header(title="Collectors without a name") %>%
  fmt_integer(n)
```

# Geographical overview

```{r}
d <- p_loc %>% 
  count(loc_id) %>% 
  inner_join(locations) %>%
  select(name,n) %>%
  collect()

poems_without_location <- poems %>% 
  anti_join(p_loc) %>% 
  count() %>% 
  pull()

unprojected_locations <- d %>%
  anti_join(polygons) %>%
  add_row(name=NA,n=poems_without_location)
```


```{r, fig.height=11}
polygons %>%
  left_join(d) %>%
  tm_shape() +
  tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
  tm_layout(title=str_c("Geographical overview. Missing ",unprojected_locations %>% tally(wt=n) %>% pull() %>% p," poems."))
```

## Poem locations not mapped

```{r}
unprojected_locations %>%
  arrange(desc(n)) %>%
  gt() %>%
  tab_header("Poem locations not mapped") %>%
  fmt_integer(n)
```

## Geographical overview by collection

```{r}
d <- p_loc %>% 
  left_join(poems) %>%
  count(collection,loc_id) %>% 
  ungroup() %>%
  inner_join(locations) %>%
  select(collection,name,n) %>%
  collect()

poems_without_location <- poems %>% 
  anti_join(p_loc) %>% 
  count(collection) %>% 
  collect() %>%
  mutate(name=NA_character_)

unprojected_locations <- d %>%
  anti_join(polygons) %>%
  union_all(poems_without_location)
```

```{r, fig.height=11}
poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~
    tm_shape(
      polygons %>%
        left_join(
          p_loc %>% 
            inner_join(poems %>% filter(collection==.x),by=c("p_id")) %>%
            count(loc_id) %>% 
            inner_join(locations) %>%
            select(name,n) %>%
            collect()
        )
    ) +
    tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
    tm_layout(title=str_c("Geography of ",.x,". Missing ",unprojected_locations %>% filter(collection==.x) %>% tally(wt=n) %>% pull() %>% p," poems."))
  )
```

## Poem locations not mapped by collection

```{r, results="asis"}
poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~
    unprojected_locations %>%
      filter(collection==.x) %>%
      arrange(desc(n)) %>%
      select(-collection) %>%
      gt() %>%
      tab_header(str_c("Poem locations not mapped in ",.x)) %>%
      fmt_integer(n)
  )
```

# Spatiotemporal overview

```{r}
d <- poems %>%
  left_join(p_year %>% mutate(year=if_else(year %in% c(0L,9999L),NA,year))) %>% 
  collect() %>%
  mutate(year_ntile=ntile(year,11)) %>%
  group_by(year_ntile) %>%
  mutate(years=str_c(min(year),"-",max(year))) %>%
  ungroup() %>%
  left_join(p_loc %>% collect()) %>% 
  count(years,loc_id) %>% 
  ungroup() %>%
  left_join(locations %>% select(loc_id,name) %>% collect())
```


```{r,fig.height=11}
polygons %>% 
  left_join(d %>% complete(name,years)) %>%
  tm_shape() +
  tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
  tm_layout(main.title="Geographical overviews by time",legend.outside.size=0.1) +
  tm_facets(by="years",ncol=4)
```

# Poem verse statistics

## Line types

```{r}
d <- verses %>% 
  left_join(verse_poem) %>% 
  left_join(poems) %>% 
  count(collection,type) %>% 
  ungroup() %>%
  arrange(collection,desc(n)) %>%
  collect()
```


```{r}
d %>% 
  group_by(collection) %>%
  mutate(proportion=n/sum(n)) %>%
  gt() %>%
  fmt_integer(n) %>%
  fmt_percent(proportion)
```

## Verse line lengths

```{r}
d_nr_characters <- verses_cl %>%
  mutate(nr_characters=str_length(text)) %>%
  left_join(verse_poem) %>% 
  left_join(poems) %>% 
  count(collection,nr_characters) %>% 
  ungroup() %>%
  arrange(collection,desc(n)) %>%
  collect()

d_nr_words <- word_occ %>%
  group_by(v_id) %>%
  summarise(nr_words=max(pos),.groups="drop") %>%
  left_join(verse_poem) %>%
  left_join(poems) %>% 
  count(collection,nr_words) %>% 
  ungroup() %>%
  arrange(collection,desc(n)) %>%
  collect()
```
### Verse line lengths in characters
```{r}
d_nr_characters %>% 
  filter(nr_characters<=60) %>%
  ggplot(aes(x=nr_characters,y=n)) +
  geom_col(width=1) +
  facet_wrap(~collection,scales="free_y") +
  theme_hsci_discrete(base_family="Arial") +
  scale_y_continuous(labels=scales::comma_format()) +
  xlab("Number of characters") +
  ylab("Verses") +
  labs(title="Number of characters in verse lines")
```

```{r}
d_nr_characters %>% 
  group_by(collection) %>%
  mutate(prop=n/sum(n)) %>%
  ungroup() %>%
  filter(nr_characters<=60) %>%
  ggplot(aes(x=nr_characters,y=collection,fill=collection,height=prop)) +
  geom_density_ridges(stat='identity') +
  theme_hsci_discrete(base_family="Arial") +
#  scale_y_continuous(labels=scales::percent_format()) +
  xlab("Number of characters") +
  ylab("Verses") +
  labs(title="Number of characters in verse lines")
```


### Verse lines with more than 60 characters

```{r}
d_nr_characters %>% 
  mutate(nl=if_else(nr_characters>60,n,0L)) %>%
  group_by(collection) %>%
  summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
  arrange(desc(lines)) %>%
  gt() %>%
  tab_header(title="Verse lines with more than 60 characters") %>%
  fmt_integer(lines) %>%
  fmt_percent(proportion)
```


### Verse line lengths in words
```{r}
d_nr_words %>% 
  filter(nr_words<=10) %>%
  ggplot(aes(x=nr_words,y=n)) +
  geom_col(width=1) +
  facet_wrap(~collection,scales="free_y") +
  scale_x_continuous(breaks=seq(0,10,by=2)) +
  scale_y_continuous(labels=scales::comma_format()) +
  theme_hsci_discrete(base_family="Arial") +
  xlab("Number of words") +
  ylab("Verses") +
  labs(title="Number of words in verse lines")
```

```{r}
d_nr_words %>% 
  filter(nr_words<=10) %>%
  uncount(n) %>%
  ggplot(aes(x=nr_words,y=collection,fill=collection)) +
  stat_binline(binwidth=1) +
  theme_hsci_discrete(base_family="Arial") +
  scale_x_continuous(breaks=seq(0,10,by=2)) +
  xlab("Number of words") +
  ylab("Verses") +
#  scale_y_continuous(labels=scales::percent_format()) +
  labs(title="Number of words in verse lines")
```

### Verse lines with more than 10 words

```{r}
d_nr_words %>% 
  mutate(nl=if_else(nr_words>10,n,0L)) %>%
  group_by(collection) %>%
  summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
  arrange(desc(lines)) %>%
  gt() %>%
  tab_header(title="Verse lines with more than 10 words") %>%
  fmt_integer(lines) %>%
  fmt_percent(proportion)
```

```{r}
verse_nr_words <- word_occ %>% 
  group_by(v_id) %>%
  summarise(nr_words=max(pos)) %>%
  compute_a(unique_indexes=list(c("v_id","nr_words")))

word_nr_characters <- words %>%
  mutate(nr_characters=str_length(text)) %>%
  select(w_id,nr_characters) %>%
  compute_a(unique_indexes=list(c("w_id","nr_characters")))

d <- word_occ %>%
  left_join(word_nr_characters) %>%
  left_join(verse_nr_words) %>%
  left_join(verse_poem %>% select(-pos),by=c("v_id")) %>% 
  left_join(poems) %>% 
  count(collection,nr_words,pos,nr_characters) %>%
  collect()
```

```{r}
d %>%
  group_by(collection,nr_words,pos) %>%
  mutate(prop=n/sum(n)) %>%
  ungroup() %>%
  filter(nr_words>=2,nr_words<6) %>%
  mutate(nr_words=as_factor(nr_words),pos=as_factor(pos)) %>%
  uncount(n) %>%
  ggplot(aes(x=nr_characters,y=nr_words,fill=nr_words)) +
  stat_binline(binwidth=1) +
  facet_grid(collection~pos,labeller = labeller(pos=label_both)) + 
  xlab("Number of characters in word") +
  ylab("Number of words in verse") +
  labs(
    title="Number of characters in words by their position",
    subtitle="According to length of verse and collection"
    ) +
  theme_hsci_discrete(base_family="Arial")
```

